




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """decision_transformer"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(						self   ,				UpperCamelCase=17   ,				UpperCamelCase=4   ,				UpperCamelCase=128   ,				UpperCamelCase=4096   ,				UpperCamelCase=True   ,				UpperCamelCase=1   ,				UpperCamelCase=1024   ,				UpperCamelCase=3   ,				UpperCamelCase=1   ,				UpperCamelCase=None   ,				UpperCamelCase="relu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=0.02   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=5_0256   ,				UpperCamelCase=5_0256   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     str:
     __a								=       state_dim
     __a								=       act_dim
     __a								=       hidden_size
     __a								=       max_ep_len
     __a								=       action_tanh
     __a								=       vocab_size
     __a								=       n_positions
     __a								=       n_layer
     __a								=       n_head
     __a								=       n_inner
     __a								=       activation_function
     __a								=       resid_pdrop
     __a								=       embd_pdrop
     __a								=       attn_pdrop
     __a								=       layer_norm_epsilon
     __a								=       initializer_range
     __a								=       scale_attn_weights
     __a								=       use_cache
     __a								=       scale_attn_by_inverse_layer_idx
     __a								=       reorder_and_upcast_attn

     __a								=       bos_token_id
     __a								=       eos_token_id

     super().__init__(bos_token_id=UpperCamelCase   ,				eos_token_id=UpperCamelCase   ,				**UpperCamelCase   )









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



